Thermal Conductivity Enhancement of Ternary Organic Heat Transfer Fluids Doped with Al₂O₃ Nanoparticles for Solar Thermal Energy Storage
Bibliographic record
Abstract
Thermal conductivity, heat transfer fluid, thermal energy storage (TES), concentrated solar power (CSP), nanoparticle doping Thermal Energy Storage (TES) systems are essential for mitigating the intermittency of renewable energy, particularly in Concentrated Solar Power (CSP) applications.This study explores the enhancement of thermal conductivity in novel ternary organic-based TES fluids, composed of varying ratios of oregano oil, olive oil, and castor oil, doped with 5 wt% Al₂O₃ nanoparticles.Using differential scanning calorimetry (DSC), seven undoped formulations were characterized, followed by nanoparticle doping in three selected samples.Thermal conductivity was measured over the range of 300-400 K, yielding values from 0.2886 to 0.5233 W/m•K for undoped samples, with melting points between 333.48 K and 336.21K. Upon doping, sample SO5 exhibited a 3.7% increase in thermal conductivity, whereas SO2 and SO7 showed decreases of 33.5% and 4.0%, respectively.These results highlight the critical influence of fluid composition, nanoparticle dispersion, and interfacial compatibility on TES performance.This work contributes to the development of cost-effective, high-efficiency TES fluids, offering new pathways for improving the sustainability and performance of CSP systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".